simple_model.py

#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import parl
import paddle
import paddle.nn as nn
import paddle.nn.functional as F


class MAModel(parl.Model):
    def __init__(self, obs_dim, act_dim, critic_in_dim):
        super(MAModel, self).__init__()
        self.actor_model = ActorModel(obs_dim, act_dim)
        self.critic_model = CriticModel(critic_in_dim)

    def policy(self, obs):
        return self.actor_model(obs)

    def value(self, obs, act):
        return self.critic_model(obs, act)

    def get_actor_params(self):
        return self.actor_model.parameters()

    def get_critic_params(self):
        return self.critic_model.parameters()


class ActorModel(parl.Model):
    def __init__(self, obs_dim, act_dim):
        super(ActorModel, self).__init__()
        hid1_size = 64
        hid2_size = 64
        self.fc1 = nn.Linear(
            obs_dim,
            hid1_size,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.XavierUniform()))
        self.fc2 = nn.Linear(
            hid1_size,
            hid2_size,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.XavierUniform()))
        self.fc3 = nn.Linear(
            hid2_size,
            act_dim,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.XavierUniform()))

    def forward(self, obs):
        hid1 = F.relu(self.fc1(obs))
        hid2 = F.relu(self.fc2(hid1))
        means = self.fc3(hid2)
        return means


class CriticModel(parl.Model):
    def __init__(self, critic_in_dim):
        super(CriticModel, self).__init__()
        hid1_size = 64
        hid2_size = 64
        out_dim = 1
        self.fc1 = nn.Linear(
            critic_in_dim,
            hid1_size,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.XavierUniform()))
        self.fc2 = nn.Linear(
            hid1_size,
            hid2_size,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.XavierUniform()))
        self.fc3 = nn.Linear(
            hid2_size,
            out_dim,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.XavierUniform()))

    def forward(self, obs_n, act_n):
        inputs = paddle.concat(obs_n + act_n, axis=1)
        hid1 = F.relu(self.fc1(inputs))
        hid2 = F.relu(self.fc2(hid1))
        Q = self.fc3(hid2)
        Q = paddle.squeeze(Q, axis=1)
        return Q